This visual is intended to convey the complexity of the bike share program by displaying all of the trips taken by one bike in the system (bike #3008) over the course of the year.
I started by just trying to display lines representing all of the trips taken by one bicycle, as generated by the starting point and ending point of each trip (since we don’t have data on exactly where the bike went during the trip). I choose this bike because, out of 4,045 bicycles, it had the greatest number of trips (970) during the year. This visual is intended for consumption by the public, potentially through an advertising campaign to communicate the popularity of the bike sharing program to encourage public use. In addition to mapping the trips, I also wanted to map the stations with points to make the visual clearer in terms of starting and ending points. Not all of the starting and ending points will be at stations (bikes are sometimes left outside hubs), but the majority of the trips will either begin or end at one of these points, so adding points that represent these stations can help organize these trip lines.
Here I’ve improved by changing the color, weight, and opacity of the lines, in order to make it clearer to see how many lines are present in the areas where they overlap. I’ve also added a pop-up when you click on the station points that displays the name of the stations.
All of the trips taken by one bike in the Bluebike system (#3008) in 2018.
In this final visualization, I incorporated some of the amazing feedback from my peer reviewers to make a few changes. First, I added more explaination to the plot to clarify what I was trying to display. Second, I changed the default view and zoom, and added a max bounds argument to make the map snap back to the bike share view if you go too far out of bounds.
I see this visual as being intended for the general public (in the mock up advertising campaign, I extend that intention by making a few more changes), in order to convey the complexity and sheer volume of travel completed by the bikes in the system. In fact, I used this exact visual in a presentation I just gave at the American Marketing Association conference as a quick demonstration of the complexity in the usage of a bicycle sharing program.
(Note: I’ve added the titles to the sidebar rather than the visual because it is basically impossible (as far as I’ve been able to tell by googling and reading documentation, but I would LOVE advice on this!) to add a title to a leaflet map in a reproducible way, and adding text above the leaflet map disappears when you try to interact with the map.)
These are just some of the adventures bike #3008 had last year. 177,463 Bluebike riders traveled more than 2,146,416 miles in 2018. Where will our bikes take you?
This is a mockup of a potential advertising campaign. I’ve chosen to make the visual more attention grabbing by using a different background (the stamen watercolor provider tile with leaflet). This definitely wouldn’t be a background I would use for a visual you want to get specific information from, but the point here is less about which stations are where and more about the overall impact of seeing where one bike traveled in a year. I also added fake markers with different icons at places of interest as a potential way of demonstrating some of the adventures the bluebikes can take you on in a potential advertising campaign. Users can interact and click to read more about a couple events that this bike (fictionally!) completed during its year in the system. I think this would be a really cute way of encouraging use of the bike share program, and encouraging use for activites people might not traditionally think of (thinking of the rides as an adventure in and of itself, where the bicycle is a partner for that adventure, rather than just a means of transportation).
This visualization is intended to communicate the distribution of bikes at stations compared to the actual usage at each station in terms of number of trips.
This visualization is intended for usage by experts, policy-makers, and managers of the bicycle sharing program. I wanted to map each of the stations in the city and compare the station’s size in terms of number of docks (representing the number of bikes that can be parked at each station), and the bike station’s usage in terms of the number of rides that originate from or end at that station. This first graph is an attempt at mapping station size, to which I want to add color indicating the number of rides started from each station. As we can see, the radius size for each circle is also way too large for it to be readable. The transparency helps, but I will work on changing the raidus size in the next visualization too.
So here I have added color using the viridis (color-blind friendly) palette, and a legend to indicate what the colors indicate in terms of the number of rides started from each station. I also used a linear transformation () to make the radius for each circle smaller and more readable. I also added a popup when you click on a station that tells you the station’s name, number of trips started, and number of docks. There are still a few things I want to change - I want to update the map like I did for visual 2 so that there are limits and it snaps back to position when you move around on it, and I want to explore other color palettes as well.
Map of BlueBike Bike Share Stations in Boston by Station Size and Number of Rides Started in 2018 Larger station markers indicate stations with more bicycle docks.
Here is my final visualization for the graph of station size by number of rides started at that station. I’ve changed a couple things, namely I added a default view and map limits so you can’t get lost when scrolling through the map (it automatically snaps you back to the default view if you go too far out of bounds). I also adjusted the color palette. In class, we talked about the importance of trying to keep interpretations as native as possible, so I though if we are talking about frequency of use (how many rides are started), I think it makes more sense to use a palette that communicates that kind of “heat” idea. While I think viridis is a gorgeous palette, I think the viridis “inferno” palette which I’ve used here does a better job of conveying how “hot” a station is, in taht cooler colors (black, dark purple) represent stations with low use, and warmer colors (orange, yellow) represent stations with more frequent use. I think that color change reduces cognitive load and makes the map more easily interpretable (but of course there is also a legend to help clarify that interpretation as well). I’ve added a title and clarified some of the labeling thanks to the advice from my reviewers as well. The one thing I choose not to change was my choice of color vs. size indicators for my two variables (size of station and number of rides started). One of my reviwers indicated that they were confused by the choice I made, and I struggled for a long time with if I should change my variable mapping or not. In the long run, after seeking advice from a number of my peers, I decided not to, mostly because I want to keep the variables as similar to their representation as possible. For me, that means that the size of the station (number of docks) should be represented by size on the graph, where bigger dots indicate bigger stations. Simultaneously, the frequency of use, or number of rides started kind of indicates how “hot” a station is, which to me feels like a native use of color (hotter = more popular/frequent). I don’t necessarily know that this is the right decision, but after pondering this suggestion for a long time, this is the decision I am the most comfortable with for this visualiztion.
In terms of interpreting this graph, as I suspected, when we graph these two things together, we see that the largest stations have the lowest number of rides started/stopped (e.g. South Station with 46 docks, but only 1,944 rides started in 2018) and the busy stations have very few docks (e.g. Dudley Town Common with 15 docks and 53,846 rides started). This might indicate that the Bluebikes system needs to reevaluate the distribution of its bikes, either through the addition/removal of docks, or the addition of new stations in high usage areas without close surrounding alternate stations (like the Dudley Town Common Station).
Map of BlueBike Bike Share Stations in Boston by Station Size and Number of Rides Completed in 2018 Larger station markers indicate stations with more bicycle docks.
As a bonus visualization, I also wanted to look at the map of station size by number of rides completed at each station. Turns out they look pretty similar, but I thought it might be important from a policy and city planning persepctive to make sure we weren’t missing something in the data in terms of stations where rides are mostly completed/ended but not vice versa.
This visualization is intended to communicate the distribution of bikes at stations compared to the actual usage at each station in terms of number of trips.
This visualization is intended for usage by experts, policy-makers, and managers of the bicycle sharing program. I wanted to map each of the stations in the city and compare the station’s size in terms of number of docks (representing the number of bikes that can be parked at each station), and the bike station’s usage in terms of the number of rides that originate from or end at that station. This first graph is an attempt at mapping station size, to which I want to add color indicating the number of rides started from each station. As we can see, the radius size for each circle is also way too large for it to be readable. The transparency helps, but I will work on changing the raidus size in the next visualization too.